The basic topology of the proposed attention guiding decoder is based on the feature fusion proposed in FCN[1]}. The feature representations extracted from the deepest three layers in the network decoder is fed to the module. As the feature maps differ in spatial size, linear interpolation is first performed to upsample the feature maps. Then the features are compressed to inhibit irrelevant information in the channel dimension and reduce computational cost. The compressed features are then concatenated respectively in the channel dimension for feature fusion. Finally the three feature maps are fused together to acquire the final prediction.
